scholarly journals Developing Software Bug Prediction Models Using Various Software Metrics as the Bug Indicators

2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Varuna Gupta ◽  
Dr. N. ◽  
Dr. Tarun
Keyword(s):  
Software Metrics ◽  
Prediction Models ◽  
Software Bug Prediction ◽  
Software Bug

Continuous Software Bug Prediction

2021 ◽  
Author(s):  
Song Wang ◽  
Junjie Wang ◽  
Jaechang Nam ◽  
Nachiappan Nagappan
Keyword(s):  
Software Bug Prediction ◽  
Software Bug

Application of Artificial Immune Systems Paradigm for Developing Software Fault Prediction Models

2012 ◽  
pp. 371-387 ◽  
Author(s):  
Cagatay Catal ◽  
Soumya Banerjee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Metrics ◽  
Prediction Models ◽  
Artificial Immune Systems ◽  
Fault Prediction ◽  
Artificial Immune ◽  
Software Fault Prediction ◽  
Immune Systems ◽  
Software Fault

Artificial Immune Systems, a biologically inspired computing paradigm such as Artificial Neural Networks, Genetic Algorithms, and Swarm Intelligence, embody the principles and advantages of vertebrate immune systems. It has been applied to solve several complex problems in different areas such as data mining, computer security, robotics, aircraft control, scheduling, optimization, and pattern recognition. There is an increasing interest in the use of this paradigm and they are widely used in conjunction with other methods such as Artificial Neural Networks, Swarm Intelligence and Fuzzy Logic. In this chapter, we demonstrate the procedure for applying this paradigm and bio-inspired algorithm for developing software fault prediction models. The fault prediction unit is to identify the modules, which are likely to contain the faults at the next release in a large software system. Software metrics and fault data belonging to a previous software version are used to build the model. Fault-prone modules of the next release are predicted by using this model and current software metrics. From machine learning perspective, this type of modeling approach is called supervised learning. A sample fault dataset is used to show the elaborated approach of working of Artificial Immune Recognition Systems (AIRS).

Hybrid Multi-Objective Grey Wolf Search Optimizer and Machine Learning Approach for Software Bug Prediction

2021 ◽  
pp. 314-337
Author(s):  
Mrutyunjaya Panda ◽  
Ahmad Taher Azar
Keyword(s):  
Error Detection ◽  
Grey Wolf ◽  
Feature Selection Technique ◽  
Bug Detection ◽  
Software Bugs ◽  
Search Optimization ◽  
Multi Class Classification ◽  
Software Bug Prediction ◽  
Robust Prediction ◽  
Software Bug

Software bugs (or malfunctions) pose a serious threat to software developers with many known and unknown bugs that may be vulnerable to computer systems, demanding new methods, analysis, and techniques for efficient bug detection and repair of new unseen programs at a later stage. This chapter uses evolutionary grey wolf (GW) search optimization as a feature selection technique to improve classifier efficiency. It is also envisaged that software error detection would consider the nature of the error when repairing it for remedial action instead of simply finding it either faulty or non-defective. To address this problem, the authors use bug severity multi-class classification to build an efficient and robust prediction model using multilayer perceptron (MLP), logistic regression (LR), and random forest (RF) for bug severity classification. Both tests are performed on two software error datasets, namely Ant 1.7 and Tomcat.

scholarly journals Software Bug Prediction Prototype Using Bayesian Network Classifier: A Comprehensive Model

2018 ◽  
Vol 132 ◽  
pp. 1412-1421 ◽  
Author(s):  
Sushant Kumar Pandey ◽  
Ravi Bhushan Mishra ◽  
Anil Kumar Triphathi
Keyword(s):  
Bayesian Network ◽  
Comprehensive Model ◽  
Bayesian Network Classifier ◽  
Software Bug Prediction ◽  
Software Bug

scholarly journals Software bug prediction using object-oriented metrics

Sadhana ◽  
2017 ◽  
Vol 42 (5) ◽  
pp. 655-669 ◽  
Author(s):  
Dharmendra Lal Gupta ◽  
Kavita Saxena
Keyword(s):  
Object Oriented ◽  
Software Bug Prediction ◽  
Software Bug ◽  
Object Oriented Metrics

scholarly journals Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach

2019 ◽  
Vol 9 (13) ◽  
pp. 2764 ◽  
Author(s):  
Abdullateef Oluwagbemiga Balogun ◽  
Shuib Basri ◽  
Said Jadid Abdulkadir ◽  
Ahmad Sobri Hashim
Keyword(s):  
Software Metrics ◽  
Prediction Models ◽  
Predictive Performance ◽  
Search Method ◽  
Feature Subset Selection ◽  
Defect Prediction ◽  
Feature Subset ◽  
Software Defect Prediction ◽  
Software Defect

Software Defect Prediction (SDP) models are built using software metrics derived from software systems. The quality of SDP models depends largely on the quality of software metrics (dataset) used to build the SDP models. High dimensionality is one of the data quality problems that affect the performance of SDP models. Feature selection (FS) is a proven method for addressing the dimensionality problem. However, the choice of FS method for SDP is still a problem, as most of the empirical studies on FS methods for SDP produce contradictory and inconsistent quality outcomes. Those FS methods behave differently due to different underlining computational characteristics. This could be due to the choices of search methods used in FS because the impact of FS depends on the choice of search method. It is hence imperative to comparatively analyze the FS methods performance based on different search methods in SDP. In this paper, four filter feature ranking (FFR) and fourteen filter feature subset selection (FSS) methods were evaluated using four different classifiers over five software defect datasets obtained from the National Aeronautics and Space Administration (NASA) repository. The experimental analysis showed that the application of FS improves the predictive performance of classifiers and the performance of FS methods can vary across datasets and classifiers. In the FFR methods, Information Gain demonstrated the greatest improvements in the performance of the prediction models. In FSS methods, Consistency Feature Subset Selection based on Best First Search had the best influence on the prediction models. However, prediction models based on FFR proved to be more stable than those based on FSS methods. Hence, we conclude that FS methods improve the performance of SDP models, and that there is no single best FS method, as their performance varied according to datasets and the choice of the prediction model. However, we recommend the use of FFR methods as the prediction models based on FFR are more stable in terms of predictive performance.

scholarly journals Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 91 ◽  
Author(s):  
Madhu Kumari ◽  
Ananya Misra ◽  
Sanjay Misra ◽  
Luis Fernandez Sanz ◽  
Robertas Damasevicius ◽  
...  
Keyword(s):  
Quality Evaluation ◽  
Prediction Models ◽  
Growth Curves ◽  
Calendar Time ◽  
Software Products ◽  
Bug Fixing ◽  
Different Types ◽  
Software Bug ◽  
Testing Coverage

A software bug is characterized by its attributes. Various prediction models have been developed using these attributes to enhance the quality of software products. The reporting of bugs leads to high irregular patterns. The repository size is also increasing with enormous rate, resulting in uncertainty and irregularities. These uncertainty and irregularities are termed as veracity in the context of big data. In order to quantify these irregular and uncertain patterns, the authors have appliedentropy-based measures of the terms reported in the summary and the comments submitted by the users. Both uncertainties and irregular patterns have been taken care of byentropy-based measures. In this paper, the authors considered that the bug fixing process does not only depend upon the calendar time, testing effort and testing coverage, but it also depends on the bug summary description and comments. The paper proposed bug dependency-based mathematical models by considering the summary description of bugs and comments submitted by users in terms of the entropy-based measures. The models were validated on different Eclipse project products. The models proposed in the literature have different types of growth curves. The models mainly follow exponential, S-shaped or mixtures of both types of curves. In this paper, the proposed models were compared with the modelsfollowingexponential, S-shaped and mixtures of both types of curves.

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